
\section{Progress}
	During past weeks, we've built a proof-of-concept ASR system based on
	MFCC feature and GMM for modeling spearker with fine-tuned parameters.

	A brief performance summary:
	\begin{itemize}
		\item $10s\sim15s$ training corpus per person.
		\item $2s\sim5s$ test corpus per person.
		\item We adopted GMM with $32$ Gaussians.
		\item Accuracy on 5 speakers is about 93 percent
		\item Accuracy on 10 speakers is about 90 percent
	\end{itemize}

	It turns out that, our method worked well when the condition that the number of
	speakers is limited to 5 or less is met. But as we employed the new dataset
	which provided by teacher this week, which contains 102 speaker, comprised
	of 60 females and 42 males, in three speaking conditions: reading,
	spontaneous and whisper, the challenge we are facing is beyond our expectation.

	Although we fulfilled the plan we aforementioned in opening report, the overall
	performance is still unsatisfying.
	The main reason that MFCC + GMM approach suffers from new corpus is that,
	using solely MFCC limits the model we can use to generative models,
	which in turns brings computation inefficiency to feature extraction, model
	training and testing.


